Method and system for failure prediction with an agent

ABSTRACT

A system, a computer program product and a method for failure prediction are implemented on an agent. The agent is installed on a machine to be monitored. The method includes detecting service data on the machine. A reference database is accessed, and a failure pattern is provided. The detected service data are analyzed in view of the provided failure pattern by applying a correlation mechanism, such that a prediction for future failures is generated and depicted as a result.

This application claims the benefit of DE 10 2009 035 949.4 filed Aug.3, 2009, which is hereby incorporated by reference.

BACKGROUND

The present embodiments relate to automatic prediction of failures in adistributed system comprising a plurality of complex machines.

Complex technical apparatuses and systems (e.g., imaging systems inradiology or other medical domains) are monitored with respect topossible failures, deficiencies, breakdowns or malfunctions. Thesemedical systems are usually monitored in the context of an eventmanagement process, which generally aims at detecting failures as earlyas possible in order to avoid future failures. Normally, well-definedevents (e.g., failure notifications) will be sent to a service operatorand will be analyzed by the same.

However, operators are often overwhelmed by their tasks to recover thesystem failure under time pressure. Therefore, it is necessary to detectfailures as soon as and as definite as possible and to avoid wrong orincomplete failure notifications.

In state of the art systems, failure patterns for a specific, particulardevice are detected. Known service management systems such as, forexample, “HP OpenView” and “ECS Designer” include functionalities inorder to detect complex failure states. These systems typically usedifferent statistical approaches, like regression and/or classificationprocedures or specific data mining procedures (e.g., expectationmaximization, survival analysis for the prediction of failures).However, these systems mainly refer to offline-analysis-systems or tosystems, which are operated separately and are not integrated in thecontext of an existing IT-architecture for the respective machines to bemonitored.

SUMMARY AND DESCRIPTION

Therefore, there is a need for a system, a method and a computer programproduct for failure prediction on an on-line basis in order to predictfailures before the failure occurs and in order to take preventiveactions for the systems as a whole. Further, the failure predictionsystem should be as simple as possible but in parallel applicable to aplurality of different kinds of technical fields and machines. Moreover,there is a need to combine existing IT-infrastructure with failureprediction. Particularly, failure prediction (as an extension) should beintegrated into or be part of an existing monitoring platform.

The present embodiments focus on a computer-based system, a computerprogram product and a computer-implemented method for failureprediction, implemented as an agent. The agent may be installed on onemachine (which results in a bidirectional relation between agent andmachine). However, an agent that is responsible for a plurality ofmachines and serves the plurality of machines in the context of failureprevention may be provided. According to another embodiment, at leastone central agent that is responsible for a plurality of machines withcomplex technical sub-modules (e.g., a scanner or a detector in adistributed client-server architecture) may be provided. The agent maybe stored as instructions for operating or configuring a programmedcomputer or processor. The agent is stored in non-transitory volatile ornon volatile memory.

In order to monitor or inspect the respective machine with respect tofailures and failure prediction, the method includes detecting servicedata on the machine, where the service data includes sensor data andnominal/time-based event data, and the service data refer to allrelevant or selected sub-modules of the machine. The method alsoincludes accessing a reference database, in which reference service datarelating to previously detected failures and maintenance related eventsare stored. The method further includes providing at least one failurepattern, analyzing the detected service data in view of the at least onefailure pattern by applying a correlation mechanism and generating aprediction for future failures as a result of the method according tothe present embodiments.

The failure prediction according to the present embodiments refers to afailure forecast so that any failure, malfunction or breakdown, forexample, of the machine is indicated or predicted before the failureoccurs. One embodiment may refer to predictive failure or servicemanagement. Another embodiment refers to the failure being indicatedbefore the failure is perceived by the operator. According to anotherembodiment, the method may also be used for proactive failuremanagement. The present embodiments refer to all kinds of failures suchas, for example, deficiencies in at least one element, module orsub-module of the machine, breakdowns, and malfunctions of theconnecting network system or of the underlying electricity.

In one embodiment, the agent is a software module that is installed onthe respective machine to be monitored. However, in other embodiments,the software module may also be installed on any other computerassociated with the machine to be monitored. According to anotherembodiment, the agent may be a hardware implementation such as, forexample, a microprocessor with the functionality mentioned above inconnection with the method according to the present embodiments.

The term “service data” may be data that is relevant for correctoperation of the machine. “Service data” may include all relevanttechnical failure-related data. Two categories of service data exist:

1. Sensor data, which are detected by sensors attached on themachines—The sensor data include temperature related data, voltage orspeed related values, material defects and computer processing data suchas, for example, resource related data, data relating to memory capacityor processing power or data relating to data transfer.

2. Nominal event data—The nominal event data include reference eventsrelated to the operation of the respective machine using messageidentificators and/or message notifications generated by sub-componentsor sub-modules such as, for example, embedded hardware controllers,workstations or robotic devices.

The service data are time-based, so that any detected event or sensorvalue is associated with a respective point of time. The point of timerefers to the occurrence of the event or the detection of the sensordata.

The reference database may be a relational database for storingreference service data relating to previously detected failures andmaintenance related events. In one embodiment, relevant context data forservicing the respective machine are additionally stored in thedatabase. The database may be a central database that is in dataexchange with all agents. According to another embodiment, the centralreference database may only be in data exchange with a central unit thatis configured to be responsible for failure prediction and serves therespective clients or agents.

The “correlation mechanism” may be an event correlation procedure and isbased on the information technological concept that any event relatingto the operation of the machine is to be regarded in the context of thepreceding and successive events of the machine. Therefore, a correlationconsists of a set of events that have a certain structure. Thisreproducible sequence forms a pattern. With these patterns, failuresituations may be identified. The present embodiments may be used toidentify failure situations that occur repeatedly and are thereforepredictable by analyzing historical (previously detected) data. Thepresent embodiments are based on the modeling of the rearward experiencethat certain events are very frequent just before the failure occurs andthat the certain events are measurable in a weaker unit as predictiveindicators.

Based on historical maintenance data and the detected service data, therespective parameters are compared for identifying a correlation ofthose parameters, values of parameters or sets of parameters that havepreviously been identified in a failure situation. In other words, thefailure situation is identified as being causally determined by theseevents.

After having identified the correlations, a prediction for futurefailures may be generated and displayed on a monitor. Alternatively, theresult may be outputted in another format (e.g., in acoustic form or byusing an alert signal).

According to one embodiment, the result also includes a probability datastructure. The probability data structure includes probability data withrespect to the forecasting or the generated prediction. According tothis aspect, a service operator or field engineer may be informed aboutthe probability of the generated failure prediction. There may bedefined rules and parameters, according to which it is configurable whento activate any appropriate counter-measure. The present embodiments mayinclude medical apparatuses and systems such as, for example, imagingsystems such as computer tomographs, nuclear magnetic resonanceapparatuses, ultrasonic devices, positron emission tomography devices orany other medical systems. These medical apparatuses and systems arecomplex systems and consist of a plurality of sub-modules, which are tooperate correctly in order to avoid any failures of the system as awhole. The interrelation of these sub-modules is taken into account byaccessing system failure of the overall system (e.g., the machine). Thisinterrelation is to be taken into account. Thus, the failure pattern mayinclude a set of patterns that refer to different sub-modules. Theevents may additionally refer to other machines or to other technicaldevices that are used for the correct operation of the machine as awhole (e.g., network devices, authorizing and authentication devices).The parameters and information representing the operation of thesub-modules or sub-components is aggregated in the respective failurepattern.

The present embodiments may be divided into two time phases:

1. A training phase—The training phase is adapted to acquire and detecttraining data that are to be examined prior to building a pattern. Thetraining data may be based on a set of log files from different sites,different machines or different sub-modules of the same machine to bemonitored. Data relating to the service management of the machine may bestored in log files to be examined later. In this training phase, a rawpattern may be generated, and the raw pattern may be used for generatinga detailed pattern, in which dependencies between the respective eventsmay be identified and weighted according to pre-configurable semanticmeasures.

2. A prediction phase—The prediction phase is used for generating afailure prediction by using the generated failure pattern (e.g.,generated in the training phase) for event correlation. The service datamay be detected in the prediction phase in order to make sure that thedetected service data are as actual as possible. However, according toanother embodiment, the prediction for a certain time period may beexecuted. For example, specific medical device failures may occur duringnight time when most of the apparatus and devices are partially in idlemode or are utilized to capacity. In this phase, the correlation may beexecuted for night time or another specific time period and not for theanother time.

According to another aspect of the present embodiments, two categoriesof patterns exist:

1. Raw patterns; and

2. Detailed patterns, where the detailed patterns are built upon and arebased on raw patterns.

Raw Patterns—Feature selection is carried out by generating a rawpattern. The raw pattern is an unstructured set of events that may beassociated with the specific failure situation. Statistical proceduressuch as, for example, frequency distributions may be applied to identifythe raw pattern. To enlarge the sample set, significance tests may beexecuted. In one embodiment, these procedures may be executed only for aspecific time period.

Detailed Patterns—Event sequences may be identified by either usingexpert knowledge and/or by applying a time-based association algorithm(e.g., generalized sequential patterns (GSP)). A detailed pattern mayalso use statistical functions on sensor values. The classificationthresholds may be identified by using a regression method.

To prove a detailed pattern's efficiency, additional significance testsare performed.

Detailed patterns consist of events, event sequences and/or sensor orother input values. The detailed patterns are organized in ahierarchical structure of sub-patterns to minimize the CPU load of theagent. Each structural element may use a threshold definition.

According to another embodiment, the correlation is based oncontinuously retrieved metrics that may be used as a basis for amachine-specific problem classification.

In one embodiment, the agent, which is configured to execute theprediction method, may perform data mining classification methods.Further, the agent may be configured to perform time-series predictionmethods such as, for example, sliding window procedures and other morecomplex models (e.g., procedures for analyzing time series suchas—autoregressive integrated moving average (ARIMA)).

The machine includes a medical, technical device such as, for example, acomplex imaging device that consists of several sub-modules. Otherembodiments include other technical devices, for example, in othertechnical areas (e.g., hardware development, chip design and automotivesystems). Therefore, the agent is configured for the respectiveapplication. In one embodiment, this customer process alignment isexecuted automatically (e.g., without any user interaction). Thealignment may be based on the log file of the machine. In otherembodiments, methods and procedures for autonomous behavior of theagent, focusing on general software performance characteristics and/oron self-adjustment to customer's processes (e.g., machine alignment),are provided.

According to another aspect of the present embodiments, a notificationunit that is configured to generate a notification (e.g., an acousticmessage, a textual message or an alert signal), send the notification tothe machine, a service unit or another device and inform about imminentfailure. Upon receiving the notification, counter-measures may beinitiated automatically or upon receiving a confirmation signal.

The machine may include a plurality of sub-modules that interact witheach other. Thus, the failure pattern includes events of all or selectedsub-modules and the interaction between the sub-modules.

In one embodiment, the failure prediction is based on the failurepattern that takes into account context data for the specific failuresituation. Context data may be the context of a possible failuresituation such as, for example, medical workflow, a specific group ofevents, the materials used, the kind of examinations. The correlationmechanism is based on a rule-based and/or knowledge-based system. Therule-based system is configured to process association models derivedfrom data mining classification algorithms such as, for example,decision tree algorithms, rule-based algorithms (e.g., of thea-priori-family), time-based algorithms (e.g., generalized sequentialpattern algorithms (GSP) and time-series models (e.g., ARIMA)).

Generated failure pattern may be generated for different time frames ortime periods. Failure patterns may be generated using the sliding windowapproach.

In one embodiment, a system for failure prediction with an agent, wherethe agent is installed on at least one machine to be monitored, and theagent is configured to execute the methods described above.

The system may include a detection unit, a reference database and aprocessing unit. Additionally, the system may include a central serviceunit for executing the failure prediction method.

According to a further embodiment, the central service unit may providedecision support functions, based on the agent data. The system supportsconsecutive actions triggered by an alert from the agent.

In one embodiment, a computer program product is provided.

Further embodiments of the system and the computer program product mayinclude the features that have been mentioned with respect to thedescription of the embodiments of the method above. The featuresaccording to the method may be implemented in modules of a hardwarestructure with the respective functionality or of a microprocessor chipthat is configured to execute the functionality described.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of a medical machine;

FIG. 2 illustrates a diagram of input and output parameters of an agent;and

FIG. 3 is a flowchart illustrating one embodiment of a method forfailure prediction.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of a machine 10.

The machine 10 may be a complex medical apparatus such as, for example,a computer tomograph, a nuclear magnetic resonance scanner or othermedical apparatuses in the field of radiology or medical imaging. Thepresent embodiments may also be applied to other fields of technology,such that the machine 10 may also be a machine for product engineering,a machine within a production cycle or a device used for hardwareengineering (e.g., chip design), for example. The machine 10 includes aplurality of sub-entities or sub-modules that interact with each otherin order to provide the functionality of the machine. In one embodiment,the machine 10 includes a scanner, a monitor, a computer with theprocessing unit, and other hardware devices with respective sub-modules.An agent 12 is installed on the machine 10 or on a computer that isassociated with the machine 10 (e.g., including respective devices fordata transmission). This is shown in FIG. 1 by the arrow starting fromthe rectangle “12” and ending at the computer of the machine 10.

In one embodiment, the system further includes a central service unit 14that includes a central processing unit 18. The system also includes areference database 16 that is shown in the middle of FIG. 1. Componentsof the system such as, for example, the machine 10, the central serviceunit 14 and the reference database 16 interact with each other using adata communication network (e.g., a bus system 20). In one embodiment,direct data connections between the respective components (e.g., betweenthe agent 12, the computer of the machine 10, the reference database 16and the central service unit 14) may be used.

The agent 12 is configured to perform a computer-implemented method forfailure prediction with respect to the machine 10. The central serviceunit 14 may monitor a plurality of machines 10. Accordingly, a pluralityof agents 12 are installed on the plurality of machines 10. In oneembodiment only one agent 12 is installed to serve a set of machines 10.In a further embodiment, only one agent is installed on a central unitserving all machines 10, where the central agent is operable to separatethe data coming from the different machines, so that an identifyingrelation (e.g., which data belongs to which machine) is possible.

FIG. 3 shows a flowchart of one embodiment of a method for failureprediction.

In FIG. 3, method acts that may be executed on the machine/agent-sideare shown on the left-hand side, whereas method acts that may beexecuted on the central (server) unit-side are shown on the right-handside. In other embodiments, other structuring and association of themethod acts with respect to the units to be executed on may be used.

In one embodiment, service data are detected on the machine 10. This maybe done by the agent 10 on the machine side (e.g., the left-hand side inFIG. 3). The service data include sensor data such as, for example,temperature, voltage or any other physical values related to theoperation of the machine 10 or to any material used for operating themachine 10. Other service data relate to computer processing of the datasuch as, for example, technical resources (e.g., memory capacity,processing power, and data transfer transmission rate). The service datamay also relate to nominal and/or time-based event data. In other words,the service data may also be associated to any events that are relevantto the operation of the machine 10. This event data are may betime-based, so a date or a point of time may be associated with therespective event. The detection of time-based event data is done byusing message identificators and/or message notifications or texts,which are generated by the sub-modules of the machine 10. Thesub-modules of the machine 10 may be embedded hardware controllers,robotic devices, or any other hardware module, software module orrelated workstations.

Referring again to FIG. 3, the method is based on an analysis ofpreviously collected historical data that relate to the operation of themachine 10. This may be done by accessing the reference database 16, inwhich reference service data relating to historical detected failuresand maintenance related events are stored. This is shown in the upperright-hand side of FIG. 3.

After accessing the reference database 16, a failure pattern for themachine 10 to be monitored is generated and provided.

The accessing and the providing of the failure pattern may be executedin a training phase, where additional acts (e.g., analyzing the detectedservice data and generating a prediction for future failures) areexecuted in a second, prediction phase. These two phases may be executedindependently of each other, such that acts of the prediction phase maybe executed during, after or before the training phase, provided thereare already enough training data collected to perform the correlationmechanism. Otherwise, a simple comparison may be executed between actualdetected machine-related data and reference data (e.g., nominaloperating data for the machine).

The training phase and the prediction phase may overlap, such thataccessing the reference data base and providing the failure pattern maybe executed in parallel to the detection of service data on therespective machine 10. The phase overlap may help to reduce performingtime.

After having detected the relevant service data on the machine 10, andafter having provided the failure pattern, the detected service data areanalyzed in view of the at least one failure pattern by applying acorrelation mechanism.

The correlation mechanism is based on a rule-based and/orknowledge-based system, where the correlation mechanism is able toprocess association models derived from data mining classificationalgorithms such as, for example, decision tree algorithms, rule-basedalgorithms (e.g., of the a-priori-family) and/or time-based algorithms(e.g., the Generalized Sequential Patterns (GSP) algorithm ortime-series models such as, for example, autoregressive integratedmoving average (ARIMA)). In one embodiment, the correlation mechanism isbased on statistical procedures and/or data mining procedures. The datamining procedures may include, for example, frequency distributions,significance tests and other statistical algorithms. In one embodiment,algorithms relating to the field of pattern matching may be used.

The acts of analyzing, applying correlation mechanisms and generatingthe prediction are shown in the middle of FIG. 3. This orientationrepresents the fact that these acts may be carried out on the agent 10or may be executed on the central service unit 14.

In one embodiment, the acts within the training phase may be executed onthe agent. In this case, there is no need to provide a separate centralservice unit 14.

The prediction for future failures is provided as a result of the methodand may be displayed on a monitor or forwarded to a user oradministrator of the system. The failure prediction includes informationwith respect to a future failed state (e.g., in which component of themachine 10 a failure will occur, at what time/when the failure willoccur, possible reasons for the failure and y possible counter-measureactions that may be taken to avoid the failure). In one embodiment, theresult may include statistical information relating to a probability ofthe occurrence of the failure.

With the aid of FIG. 2, the input and output parameters are described inmore detail in order to describe the failure prediction method accordingto one embodiment. The input parameters may include: log files ofhistorical maintenance events and monitoring events or log filesregarding previously detected failures; hardware events; softwareevents; machine-related events; or additional event sources that mayrelate to associated sub-modules of the machine 10 or to otherassociated external modules.

The input parameters are processed by the agent on the machine 10. Thisis done using a rule engine of a statistic engine, a classificationengine and a Complex Event Processing (CEP)-engine. Any other algorithmsmay be used for processing the input parameters in order to derive afailure prediction of sub-modules of the machine 10.

On the right-hand side in FIG. 2, the output parameters are shown. Theoutput parameters may include: decision support for a maintenance of themachine 10; a prediction of failures (each result includes informationwith respect to the sub-module of the machine, where the failure mayoccur); and/or probability measures and/or results of other processingdevices. For example, the output may be forwarded to a decision supportmodule. The decision support module may trigger consecutive actions withregard to the predicted failure and may be associated to an alert (e.g.,at the side of the agent).

According to another embodiment, the result also includes features forproviding an autonomous behavior of the agent. The term “autonomousbehavior” is to be interpreted in the sense of automatically triggeringactions and measures in order to avoid the predicted failure at themachine 10. The autonomous behavior relates to an adjustment of theactual system parameters such as, for example, general software orhardware performance characteristics. The autonomous behavior alsorelates to a self-adjustment to needs and requirements of the client orcustomer, processes of the client, customer, or the specificapplication, application or use of the machine 10 (e.g., within aspecific medical workflow).

The generated and provided failure patterns relate to failure-relatedevents. The failure-related events may be structured into semanticblocks with dependencies between events. Additionally, elements of thefailure pattern may be weighted for adjustment of the respective patternfor a dedicated use case.

In one embodiment, the events within the failure pattern are structured.The events within the failure pattern may be structured according totime-periods or to pre-configurable time-intervals.

In another embodiment, the result with the failure pattern and thegenerated prediction is displayed on a display device that is associatedwith the machine 10 or the central service unit. The amount ofdisplaying may be configured, for example, to select specific timeframes and data to be displayed. For example, data after Jun. 1, 2010and before Jun. 5, 2010 may be selected. The selected time-period may bedisplayed as a window width, which may be highlighted during displaying.Within the chosen window width, the failure patterns are displayed inmore detail and may be processed by further statistical algorithms.

In one embodiment, only the service data that are derived from therespective machine 10 are used for failure prediction of the machine 10.This embodiment relates to an intra-machine failure prediction.

In another embodiment, the service data to be used for generating theprediction may be extended. Other service data, stemming from otheragents or machines (e.g., within the network), are analyzed and used forgenerating the prediction. This embodiment is useful if service data ofmachine A have to be compared to reference service data of anothermachine B (e.g., only differing in that a software update has beenimplemented).

The comparison or correlation may be executed relative to a specifickind of machine, a different kind of machine or different machines indifferent fields. Thus, the same failure prediction method may be usedwithout further adaption, for example, for CT scanners, NMR scanners,train maintenance systems, automotive systems, wind energy converters,chip engineering or any other technical application. Due to theself-alignment of the present embodiments, it is not necessary to adaptthe process to the specific kind of machine; therefore, the process maybe used for a number of different systems and machines in differentfields. The prediction is based on historical machine-specific data anda specific time function (e.g., a sliding windows approach).

In one embodiment, the functionality of the agent may be extended oradditional agents may be provided, for example, for comparing thefailure pattern with threshold values, which may be pre-configured, forpattern management and/or for communication with a service managementsystem. Additionally, agents may be provided for performance monitoringof the agent or for performance monitoring of the machine (e.g., CPUmonitoring and/or memory monitoring). Further agents may relate to themanagement of the agents to be installed on the respective machines 10.The management of the agents may include starting, stopping and manuallyamending the functionality of the agent.

An advantage of the present embodiments is that the failure predictionmay be integrated in the existing information-technology architecture.It is not necessary to install an additional separate system. To thecontrary, the functionality of the present IT-system may be extended byproviding an agent on the client side.

Another advantage is that the machines 10 may provide an autonomousfunctionality with respect to failure management.

While the present invention has been described above by reference tovarious embodiments, it should be understood that many changes andmodifications can be made to the described embodiments. It is thereforeintended that the foregoing description be regarded as illustrativerather than limiting, and that it be understood that all equivalentsand/or combinations of embodiments are intended to be included in thisdescription.

1. A computer-implemented method for failure prediction, implemented asan agent, wherein the agent is installed on a machine to be monitored,the computer-implemented method comprising: detecting service data onthe machine; accessing a reference database, in which reference servicedata relating to previously detected failures and maintenance relatedevents are stored; providing a failure pattern; analyzing the detectedservice data in view of the failure pattern, the analyzing comprisingapplying a correlation mechanism; generating a prediction for futurefailures as a result of the analyzed detected service data; andautomatically triggering measures to avoid the predicted futurefailures, wherein automatically triggering measures comprisesautomatically adjusting parameters of the machine to be monitored. 2.The method of claim 1, further comprising generating a notificationinforming about a future failure and sending the notification to themachine, a service unit or to another device.
 3. The method of claim 1,wherein the machine comprises a plurality of sub-modules, the pluralityof sub-modules interacting with each other, and wherein the failurepattern comprises events of all or selected sub-modules and theinteractions between all or the selected sub-modules.
 4. The method ofclaim 1, wherein the failure pattern is generated by taking into accountmachine-related parameters and events and prior failure-related,statistical data.
 5. The method of claim 1, wherein the failure patternis generated by taking into account different time phases, during whichpreviously detected failures and events have occurred and during whichmachine-related service data are detected or have occurred.
 6. Themethod of claim 1, wherein the detected service data comprise contextdata with respect to the machine or an actual functionality or a presentapplication of the machine.
 7. The method of claim 1, wherein thedetected service data comprise: sensor data including temperature,voltage or speed values or material defects; computer processing dataincluding resources, memory capacity, processing power or data transfer;and nominal, time-based event data, generated by sub-modules of themachine, the sub-modules including embedded hardware controllers orrobotic devices.
 8. The method of claim 1, wherein the correlationmechanism uses a threshold for machine-related parameters or servicedata, and wherein the threshold represents a limit for correct operationof the machine.
 9. The method of claim 1, wherein the correlationmechanism is based on a rule-based or knowledge-based system, andwherein the rule-based system is operable to process association modelsderived from data mining classification algorithms, the data miningclassification algorithms including decision tree algorithms, rule-basedalgorithms, time-based algorithms, or time-series models.
 10. The methodof claim 1, wherein the correlation mechanism uses statistical or datamining procedures, and wherein the statistical procedures compriseanalysis of frequency distributions, significance tests and ageneralized sequential pattern algorithm.
 11. The method of claim 1,wherein the generated prediction for future failures is generated fordifferent time frames using a sliding windows approach.
 12. The methodof claim 1, wherein the machine is a medical apparatus.
 13. The methodof claim 1, wherein the result is used to pre-plan upcoming serviceactions with respect to the machine.
 14. The method of claim 1, furthercomprising providing procedures for autonomous behavior of the agent,taking into account performance characteristics and self-adjustment toclient applications on the machine or to the machine.
 15. The method ofclaim 1, wherein the method is used for different machines.
 16. A systemfor failure prediction with an agent, wherein the agent is installed ona machine to be monitored, the system comprising: a detection unit fordetecting service data on the machine; a reference database, in whichreference service data relating to previously detected failures andmaintenance related events are stored; a processing unit configured for:providing a failure pattern; analyzing the detected service data in viewof the failure pattern as a function of correlation; and generating aprediction for future failures as a result of the analyzed detectedservice data, the generated prediction for future failures beinggenerated for different time frames using a sliding windows approach;and a central service unit for failure prediction, the central serviceunit being in data exchange with a plurality of agents for a pluralityof different machines, such that a plurality of failure patternscorresponding to the plurality of different machines is provided, theplurality of agents comprising the agent, the plurality of differentmachines comprising the machine, the plurality of failure patternscomprising the failure pattern.
 17. The system of claim 16, wherein thesystem or the processing unit is configured to provide decision supportfunctions and support consecutive actions triggered by an agent alert,based on the data of the agent.
 18. A non-transitory computer programproduct for failure prediction, implemented on an agent, wherein theagent is installed on a machine to be monitored, wherein the computerprogram product comprises a computer-readable storage medium havingcomputer-readable program code portions stored therein, thecomputer-readable program code portions comprising: detecting servicedata on the machine; accessing a reference database, in which referenceservice data relating to previously detected failures and maintenancerelated events are stored; providing at least one failure pattern;applying a correlation mechanism to the detected service data with thefailure pattern; and generating a prediction for future failures as aresult of the correlation mechanism being applied to the detectedservice data, the prediction for future failures comprising statisticalinformation relating to a probability of the occurrence of the futurefailures, wherein the generated prediction for future failures isgenerated for different time frames using a sliding windows approach.19. The system of claim 16, wherein the central service unit comprisesthe processing unit.